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Relevant to mobile health, the design of a portable electrocardiograph (ECG) device using AD823X microchips as the analog front-end is presented. Starting with the evaluation board of the chip, open-source hardware and software components were integrated into a breadboard prototype. This required modifying the microchip with the breadboard-friendly Arduino Nano board in addition to a data logger and a Bluetooth breakout board. The digitized ECG signal can be transmitted by serial cable, via Bluetooth to a PC, or to an Android smartphone system for visualization. The data logging shield provides gigabytes of storage, as the signal is recorded to a microSD card adapter. A menu incorporates the device’s several operating modes. Simulation and testing assessed the system stability and performance parameters in terms of not losing any sample data throughout the length of the recording and finding the maximum sampling frequency; and validation determined and resolved problems that arose in open-source development. Ultimately, a custom printed circuit board was produced requiring advanced manufacturing options of 2.5 mils trace widths for the small package components. The fabricated device did not degrade the AD823X noise performance, and an ECG waveform with negligible distortion was obtained. The maximum number of samples/second was 2380 Hz in serial cable transmission, whereas in microSD recording mode, a continuous ECG signal for up to 36 h at 500 Hz was verified. A low-cost, high-quality portable ECG for long-term monitoring prototype that reasonably complies with electrical safety regulations and medical equipment design was realized.

According to the American Heart Association, in its latest commission about Ventricular Arrhythmias and Sudden Death 2006, the epidemiology of the ventricular arrhythmias ranges from a series of risk descriptors and clinical markers that go from ventricular premature complexes and nonsustained ventricular tachycardia to sudden cardiac death due to ventricular tachycardia in patients with or without clinical history. The premature ventricular complexes (PVCs) are known to be associated with malignant ventricular arrhythmias and sudden cardiac death (SCD) cases. Detecting this kind of arrhythmia has been crucial in clinical applications. The electrocardiogram (ECG) is a clinical test used to measure the heart electrical activity for inferences and diagnosis. Analyzing large ECG traces from several thousands of beats has brought the necessity to develop mathematical models that can automatically make assumptions about the heart condition. In this work, 80 different features from 108,653 ECG classified beats of the gold-standard MIT-BIH database were extracted in order to classify the Normal, PVC, and other kind of ECG beats. Three well-known Bayesian classification algorithms were trained and tested using these extracted features. Experimental results show that the F1 scores for each class were above 0.95, giving almost the perfect value for the PVC class. This gave us a promising path in the development of automated mechanisms for the detection of PVC complexes.

No abstract

Lab-on-a-Chip (LoC) devices are described as versatile, fast, accurate, and low-cost platforms for the handling, detection, characterization, and analysis of a wide range of suspended particles in water-based environments. However, for gas-based applications, particularly in atmospheric aerosols science, LoC platforms are rarely developed. This review summarizes emerging LoC devices for the classification, measurement, and identification of airborne particles, especially those known as Particulate Matter (PM), which are linked to increased morbidity and mortality levels from cardiovascular and respiratory diseases. For these devices, their operating principles and performance parameters are introduced and compared while highlighting their advantages and disadvantages. Discussing the current applications will allow us to identify challenges and determine future directions for developing more robust LoC devices to monitor and analyze airborne PM.